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Found 14 papers, 4 papers with code
THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech
Detecting harmful content on social media, such as Twitter, is made difficult by the fact that the seemingly simple yes/no classification conceals a significant amount of complexity.
Predicting infections in the Covid-19 pandemic -- lessons learned
Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions.
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments
The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavior.
Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity
Recently, the Maxmin and Ensemble Q-learning algorithms have used different estimates provided by the ensembles of learners to reduce the overestimation bias.
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation.
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward
This is a challenging task for current state-of-the-art multi-agent reinforcement algorithms that are designed to either maximize the global reward of the team or the individual local rewards.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter
In addition, we find that both ASOR-IA and ASOR-EA outperform previous approaches even in uncluttered environments, with ASOR-EA performing better even in clutter compared to the previous best baseline in an uncluttered environment.
Universal Policies to Learn Them All
We explore a collaborative and cooperative multi-agent reinforcement learning setting where a team of reinforcement learning agents attempt to solve a single cooperative task in a multi-scenario setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning
We are considering a scenario where a team of bodyguard robots provides physical protection to a VIP in a crowded public space.
Unsupervised Meta-Learning For Few-Shot Image Classification
In this paper, we propose UMTRA, an algorithm that performs unsupervised, model-agnostic meta-learning for classification tasks.
Pay attention! - Robustifying a Deep Visuomotor Policy through Task-Focused Attention
In this paper, we propose an approach for augmenting a deep visuomotor policy trained through demonstrations with Task Focused visual Attention (TFA).
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation.
From virtual demonstration to real-world manipulation using LSTM and MDN
Our experimental studies validate the three contributions of the paper: (1) the controller learned from virtual demonstrations can be used to successfully perform the manipulation tasks on a physical robot, (2) the LSTM+MDN architectural choice outperforms other choices, such as the use of feedforward networks and mean-squared error based training signals and (3) allowing imperfect demonstrations in the training set also allows the controller to learn how to correct its manipulation mistakes.
A cookbook of translating English to Xapi
The Xapagy cognitive architecture had been designed to perform narrative reasoning: to model and mimic the activities performed by humans when witnessing, reading, recalling, narrating and talking about stories.
